Liang Lin

729 total citations
11 papers, 452 citations indexed

About

Liang Lin is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Urban Studies. According to data from OpenAlex, Liang Lin has authored 11 papers receiving a total of 452 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Computer Vision and Pattern Recognition, 6 papers in Artificial Intelligence and 1 paper in Urban Studies. Recurrent topics in Liang Lin's work include Human Pose and Action Recognition (3 papers), Domain Adaptation and Few-Shot Learning (3 papers) and Advanced Neural Network Applications (3 papers). Liang Lin is often cited by papers focused on Human Pose and Action Recognition (3 papers), Domain Adaptation and Few-Shot Learning (3 papers) and Advanced Neural Network Applications (3 papers). Liang Lin collaborates with scholars based in China, United States and Singapore. Liang Lin's co-authors include Shuicheng Yan, Xiaodan Liang, Jiangbo Lu, Dongbo Min, N. Minh, Jian Dong, Xiaohui Shen, Si Liu, Luoqi Liu and Jianhuang Lai and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and Neurocomputing.

In The Last Decade

Liang Lin

10 papers receiving 443 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Liang Lin China	8	419	81	73	41	26	11	452
Zhangzhang Si United States	9	348 0.8×	149 1.8×	30 0.4×	24 0.6×	31 1.2×	15	416
Chen Guo China	8	148 0.4×	54 0.7×	26 0.4×	33 0.8×	24 0.9×	24	218
Weize Quan China	11	313 0.7×	42 0.5×	30 0.4×	31 0.8×	29 1.1×	23	391
In Kyu Park South Korea	11	276 0.7×	23 0.3×	52 0.7×	24 0.6×	42 1.6×	29	330
Haoning Wu Singapore	12	397 0.9×	27 0.3×	140 1.9×	37 0.9×	10 0.4×	38	533
Yuming Jiang China	8	178 0.4×	52 0.6×	33 0.5×	27 0.7×	53 2.0×	29	360
Pingbo Pan Australia	6	513 1.2×	101 1.2×	81 1.1×	21 0.5×	12 0.5×	9	571
Orest Kupyn Ukraine	2	606 1.4×	29 0.4×	291 4.0×	24 0.6×	17 0.7×	2	666
Tetiana Martyniuk Ukraine	2	606 1.4×	29 0.4×	291 4.0×	24 0.6×	17 0.7×	5	666
Guo-Ye Yang China	6	243 0.6×	28 0.3×	52 0.7×	29 0.7×	16 0.6×	7	279

Countries citing papers authored by Liang Lin

Since Specialization

Citations

This map shows the geographic impact of Liang Lin's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Liang Lin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Liang Lin more than expected).

Fields of papers citing papers by Liang Lin

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Liang Lin. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Liang Lin. The network helps show where Liang Lin may publish in the future.

Co-authorship network of co-authors of Liang Lin

This figure shows the co-authorship network connecting the top 25 collaborators of Liang Lin. A scholar is included among the top collaborators of Liang Lin based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Liang Lin. Liang Lin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

11 of 11 papers shown

1.

Li, Jiaming, Jichang Li, Ge Li, et al.. (2024). Learning Background Prompts to Discover Implicit Knowledge for Open Vocabulary Object Detection. 16678–16687. 9 indexed citations

2.

Wang, Guangrun, Yixing Lao, Peng Chen, et al.. (2024). AlignMiF: Geometry-Aligned Multimodal Implicit Field for LiDAR-Camera Joint Synthesis. 21230–21240. 2 indexed citations

3.

Wu, Hefeng, et al.. (2024). Improving Network Interpretability via Explanation Consistency Evaluation. IEEE Transactions on Multimedia. 26. 11261–11273.

4.

Wang, Kuo, Guanbin Li, Chaowei Fang, et al.. (2023). De-biased Teacher: Rethinking IoU Matching for Semi-supervised Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence. 37(2). 2573–2580. 9 indexed citations

5.

Yang, Xiao‐Jun, Siyuan Li, Ke Liang, Feiping Nie, & Liang Lin. (2022). Structured graph optimization for joint spectral embedding and clustering. Neurocomputing. 503. 62–72. 7 indexed citations

6.

Wang, Kuo, Chaowei Fang, Xuewen Wu, et al.. (2022). Double-Check Soft Teacher for Semi-Supervised Object Detection. Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence. 1430–1436. 7 indexed citations

7.

Liang, Xiaodan, Si Liu, Xiaohui Shen, et al.. (2015). Deep Human Parsing with Active Template Regression. IEEE Transactions on Pattern Analysis and Machine Intelligence. 37(12). 2402–2414. 183 indexed citations

8.

Lin, Liang, Yuanlu Xu, Xiaodan Liang, & Jianhuang Lai. (2014). Complex Background Subtraction by Pursuing Dynamic Spatio-Temporal Models. IEEE Transactions on Image Processing. 23(7). 3191–3202. 62 indexed citations

9.

Ni, Bingbing, et al.. (2013). Integrating multi-stage depth-induced contextual information for human action recognition and localization. 1–8. 12 indexed citations

10.

Lu, Jiangbo, et al.. (2012). Cross-based local multipoint filtering. 430–437. 118 indexed citations

11.

Lin, Liang, Xiaobai Liu, & Song‐Chun Zhu. (2009). Layered Graph Matching with Composite Cluster Sampling. IEEE Transactions on Pattern Analysis and Machine Intelligence. 32(8). 1426–1442. 43 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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